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Reaping the benefits of predictive maintenance

Author : Jez Palmer, business development manager, Schneider Electric

18 June 2010

Managing processes and equipment through predictive maintenance presents significant benefits. With the rapid evolution of technology, companies can obtain a higher level of intelligence while simultaneously monitoring plants, reducing downtime and ultimately cutting costs.

Reaping the benefits of predictive maintenance
Reaping the benefits of predictive maintenance

It is not uncommon to find owners and operators looking for ways to extend the life of existing equipment as getting more out of an asset can help a business to reduce costs, improve production efficiency, performance and profitability. In addition, there is little doubt that the majority of production facilities still have enormous potential for improvement when it comes to plant performance.

In an industrial facility, maintenance can account for a significant proportion of operating costs. Lack of knowledge about when and what kind of maintenance is needed to maintain, repair or replace critical machinery, equipment and systems within a plant or facility, can result in ineffective use of maintenance expenditure and perhaps more significantly, loss of production.
Monitoring the operating condition of critical plant equipment, machinery and systems provides businesses with the knowledge to effectively manage the maintenance operation. As a minimum, it provides the means to reduce or eliminate unnecessary repairs, prevent catastrophic machine failures and reduce the negative impact of ineffective maintenance operation on the profitability of manufacturing and production plants. However, the information can also optimize total plant performance, equipment life, and life cycle costs of the facility and its assets by utilising it for a predictive maintenance programme.

Predictive maintenance is a condition-based programme. Instead of relying on the average life statistics of machinery or a process, ie mean-time-to-failure, to determine when to schedule maintenance activities, predictive maintenance monitors operating conditions, efficiencies, heat distribution and other indicators to determine the actual mean-time-to-failure. This data provides the factual information needed for effective planning and scheduling maintenance activities.
Overall Equipment Effectiveness (OEE) is now widely recognised by industries as a way of measuring plant performance and can provide meaningful information that can determine a business' predictive maintenance programme. The success of OEE relies on having access to data in real-time or as close to real-time as possible, from production equipment, and then presenting the information in a way that can be understood. This requires the use of a Manufacturing Execution System (MES), which interfaces with existing SCADA, HMIs, other process control and automation systems (such as variable speed drives) as well as business systems.

For predictive or condition-based maintenance work to be effective, it's less about new technology and more about ways of bringing data to the user to give more visibility, which is possible through MES. By collating relevant information, it is possible for intelligence-based business decisions to be made using real-time information.

The level of information provided by a MES can begin with standards reports covering areas such as alarms states and statistics, run hours, tag calculations and values for aspects including KwH and processes marked as shift parameters. In addition, users can access alarms management reports focusing on correlation, frequency, major events and longest standing, which will help identify common problem areas that may require more regular maintenance or highlight areas that could potentially fail. This ensures preventative maintenance work can be carried out before the problem results in costly and unexpected downtime.

Taking the information a step further, businesses can look at the state of a plant or specific process and analyse its behaviour. Production specific modules can provide real-time data on yield, energy consumption per output unit, output per shift, as well as actual versus targets on materials, energy, waste, emissions and product output.

Access to this level of detail can offer economic benefits in a number of ways. Information on aspects of the plant such as identifying 'lost production' - the hidden potential in a machine to deliver additional output; or quickly identifying causes that can be detrimental to production, such as operator issues and machine or material issues; or raising production consistency and reducing waste, are all factors that can have an impact on the company's predictive maintenance schedule.

As well as real-time information, historical data can be used in predictive maintenance. It can assist in planning a maintenance programme, as the records can be used to predict when issues will happen in the future, based on trends and patterns. This can help with budgeting, again controlling costs as the amount of unplanned, ad hoc work should be reduced.

The saying 'prevention is better than cure' is never more relevant than when it comes to predictive maintenance. Embracing this philosophy holds massive potential to save money and reduce man- hours, which can have a positive impact on the bottom line. As the recession forced many companies to implement tough cost-cutting measures, we should be seeing an end to the more costly 'fix it when it breaks' maintenance and a shift towards prevention and prediction.

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